Image Processing Reference
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Fig. 9.5 Original reflectance spectrum from a cobblestone pavement and continuum removed
image areas that are 100% pure concerning the respective material. Usually, even
pure materials will not perfectly fit library spectra due to diverse error components
(measurement setup, SNR, directional reflectance differences, calibration, atmo-
spheric correction, etc.). It is very likely that the majority of image pixels will rather
be mixed than pure in urban environments. Absorption features will therefore be
masked or enhanced by other material characteristics on one hand and new absorp-
tion features may appear on the other hand. There is in any case the need to account
for such effects beyond the RMSE as a global measure of spectral fit. Individual
absorption feature depth or FWHM comparisons between image and reference
spectra may hence serve as a measure of material abundance in mixed pixels.
As view angle dependent effects are critical in urban environments and illumina-
tion geometry is complex, it might be advantageous to employ methods that are
fairly insensitive to illumination effects. Spectral angle mapping (SAM) is such a
technique. Differently from other classification techniques, SAM compares refer-
ence signatures with individual pixels not by their statistical representation in feature
space per se, but by their angular differences in feature space position. Considering
multidimensional feature space as axes starting from a zero-reflectance point, refer-
ence signatures and pixels are aligned along these axes and the multidimensional
angle between all references and the respective target pixel are calculated. This angle
is independent from changes in pixel albedo, as all pixels of the same spectral char-
acter exhibiting for example illumination differences will align along the same vector
starting from the zero reflectance point. As the vector direction does not change the
angle between a reference target and a pixel vector is fixed either.
Analysis Focusing on Mixed Pixels
Ridd ( 1995 ) has proposed a conceptual framework to analyze urban remote sensing
data based on the major urban surface components vegetation, impervious surfaces
and soil. This model has became a kind of standard concept for many remote sensing
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